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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Opioid2FHIR: A system for extracting FHIR-compatible opioid prescriptions from clinical text
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Opioid2FHIR: A system for extracting FHIR-compatible opioid prescriptions from clinical text

机译：opioid2fhir：一种从临床文本中提取FHIR兼容的阿片类药物的系统

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Background: The opioid crisis is a national public health emergency in US. Especially, prescription opioids contributed significantly to drug overdose deaths. To improve the surveillance of prescription opioid overdose, it is critical to accurately collect prescription opioid information and calculate morphine milligram equivalents (MMEs). However, plenty of detailed information is only contained in the free text Sig component of electronic health record (EHR) prescriptions and need to be extracted first. Moreover, it is also indispensable to normalize opioid information extracted from multiple heath care facilities to clinical data standards such as Fast Healthcare Interoperability Resources (FHIR) for efficient clinical decision support. However, few efforts are spent in this direction at present. Methods: In this study, we designed and implemented a system that can automatically extract opioid information from free text in Sig and map them to FHIR. The system, named as Opioid2FHIR, applied multiple natural language processing (NLP) techniques for opioid information extraction and normalization. In order to reduce manual efforts, a general-purpose medication IE model was first leveraged. Based on 1, 000 opioid prescription records randomly selected from MIMICIII, post-processing rules were designed to adapt the IE model to opioid medications. Concept normalization models were also built to transform and map the extracted medication elements to fine-granular standard concepts in FHIR. The system was evaluated on another 1, 000 opioid prescription records in MIMICIII. Results: Opioid2FHIR obtained an F-measure of 0.963 for medication information extraction and an accuracy of 0.987 for medical concept normalization. Conclusions: A clinical NLP application to EHR opioid scripts would fill a current gap in available batch script processing tools and would greatly enhance individual prescription processing limitations of prescription drug monitoring programs and clinical MME calculators.
机译：背景：阿片类药物危机是美国国家公共卫生紧急情况。特别是，处方阿片类药物对药物过量死亡的贡献显着。为了改善处方阿片类药物过量的监测，准确收集处方阿片类药物信息并计算吗啡毫克等当量（MME）至关重要。但是，许多详细信息仅包含在电子健康记录（EHR）处方的自由文本SIG组件中，并且需要首先提取。此外，它还不可或缺于将从多个Heath护理设施提取的Opioid信息标准化为临床数据标准，例如快速医疗互操作性资源（FHIR），以实现有效的临床决策支持。但是，目前少量努力。方法：在本研究中，我们设计并实现了一个系统，可以自动从SIG中的自由文本中提取鸦片信息并将其映射到FHIR。该系统名为OpioID2FHIR，应用了用于Opioid信息提取和标准化的多种自然语言处理（NLP）技术。为了减少手动努力，首先利用通用药物IE模型。基于从MIMICIII中随机选择的1,000个阿片类药物处方记录，设计后的后处理规则以使IE模型适应阿片类药物。概念归一化模型也被建立在FHIR中的细粒度标准概念中变换并将提取的药物元素进行变换和映射。该系统在MIMICIII中的另外1,000个阿片类药物处方记录上进行评估。结果：OPIOD2FHIR获得了0.963的F-PEACE，用于药物信息提取和医学概念标准化的精度为0.987。结论：对EHR OpioID脚本的临床NLP应用程序将填补可用批量脚本处理工具的当前差距，并大大提高处方药监测计划和临床MME计算器的个人处方处理限制。

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